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WO2024049052A1 - Procédé, système et support d'enregistrement lisible par ordinateur non transitoire pour estimer l'arythmie au moyen d'un réseau neuronal artificiel composite - Google Patents

Procédé, système et support d'enregistrement lisible par ordinateur non transitoire pour estimer l'arythmie au moyen d'un réseau neuronal artificiel composite Download PDF

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Publication number
WO2024049052A1
WO2024049052A1 PCT/KR2023/012021 KR2023012021W WO2024049052A1 WO 2024049052 A1 WO2024049052 A1 WO 2024049052A1 KR 2023012021 W KR2023012021 W KR 2023012021W WO 2024049052 A1 WO2024049052 A1 WO 2024049052A1
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Prior art keywords
section
neural network
artificial neural
class
arrhythmia
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PCT/KR2023/012021
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English (en)
Korean (ko)
Inventor
정성훈
김진국
장재성
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Huinno Co Ltd
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Huinno Co Ltd
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Priority to JP2024508976A priority Critical patent/JP7763441B2/ja
Publication of WO2024049052A1 publication Critical patent/WO2024049052A1/fr
Priority to US18/606,993 priority patent/US20240215925A1/en
Anticipated expiration legal-status Critical
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/361—Detecting fibrillation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/366—Detecting abnormal QRS complex, e.g. widening
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention relates to a method, system, and non-transitory computer-readable recording medium for estimating arrhythmia using a complex artificial neural network.
  • these wearable monitoring devices are equipped with an artificial intelligence model to estimate arrhythmia from the electrocardiogram signal.
  • an artificial intelligence model uses an artificial intelligence model learned to estimate what type of arrhythmia a given section of the electrocardiogram signal corresponds to. It was generally implemented based on neural networks.
  • the artificial neural network learned to estimate which arrhythmia a given ECG signal section corresponds to is an arrhythmia that can be estimated on a beat segment basis (e.g., atrial premature contraction (APC), ventricular premature contraction). Since there is a limitation of not being able to accurately estimate (Ventricular Premature Contraction, VPC), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), etc.), conventional wearable monitoring devices have There is a problem that it is not possible to accurately determine the number or proportion of arrhythmias that can be estimated on a beat segment basis within the section of the ECG signal.
  • VPC atrial premature contraction
  • LBBB Left Bundle Branch Block
  • RBBB Right Bundle Branch Block
  • the purpose of the present invention is to solve all the problems of the prior art described above.
  • the present invention provides an artificial neural network learned to estimate which arrhythmia a beat segment included in a section of a given ECG signal corresponds to, and an artificial neural network learned to estimate which arrhythmia a section of a given ECG signal corresponds to.
  • the purpose is to improve the accuracy of arrhythmia estimation by using in combination.
  • a representative configuration of the present invention to achieve the above object is as follows.
  • a method for estimating arrhythmia using a complex artificial neural network comprising: estimating a class corresponding to a beat segment included in a first section of an electrocardiogram signal using a first artificial neural network; 2 estimating a class corresponding to a first section of the ECG signal using an artificial neural network, and the class estimated to correspond to a bit segment included in the first section of the ECG signal and the first section of the ECG signal
  • a method including the step of mutually verifying classes estimated to correspond to is provided.
  • a system for estimating arrhythmia using a complex artificial neural network comprising: a first estimator for estimating a class corresponding to a beat segment included in a first section of an electrocardiogram signal using a first artificial neural network; a government, a second estimator for estimating a class corresponding to a first section of the ECG signal using a second artificial neural network, and a class estimated to correspond to a bit segment included in the first section of the ECG signal and the A system including a verification unit that mutually verifies classes estimated to correspond to a first section of an electrocardiogram signal is provided.
  • an artificial neural network learned to estimate which arrhythmia a beat segment included in a section of a given ECG signal corresponds to and an artificial neural network learned to estimate which arrhythmia a section of a given ECG signal corresponds to.
  • Figure 1 is a diagram showing the schematic configuration of an entire system for estimating arrhythmia using a complex artificial neural network according to an embodiment of the present invention.
  • Figure 2 is a diagram illustrating in detail the internal configuration of an arrhythmia estimation system according to an embodiment of the present invention.
  • Figure 3 is a diagram schematically showing a mutual verification process according to an embodiment of the present invention.
  • Figure 1 is a diagram showing the schematic configuration of an entire system for estimating arrhythmia using a complex artificial neural network according to an embodiment of the present invention.
  • the entire system may include a communication network 100, an arrhythmia estimation system 200, and a device 300.
  • the communication network 100 can be configured regardless of communication mode, such as wired communication or wireless communication, and can be used as a local area network (LAN) or a metropolitan area network (MAN). ), and a wide area network (WAN).
  • LAN local area network
  • MAN metropolitan area network
  • WAN wide area network
  • the communication network 100 referred to in this specification may be the known Internet or World Wide Web (WWW).
  • WWW World Wide Web
  • the communication network 100 is not necessarily limited thereto and may include at least a portion of a known wired or wireless data communication network, a known telephone network, or a known wired or wireless television communication network.
  • the communication network 100 is a wireless data communication network, including WiFi communication, WiFi-Direct communication, Long Term Evolution (LTE) communication, 5G communication, and Bluetooth communication (Bluetooth Low Energy (BLE). It may implement, at least in part, conventional communication methods such as (including Bluetooth Low Energy) communication, infrared communication, ultrasonic communication, etc.
  • the communication network 100 is an optical communication network and may implement at least a portion of a conventional communication method such as LiFi (Light Fidelity).
  • the arrhythmia estimation system 200 may perform communication with a device 300, which will be described later, through the communication network 100.
  • the arrhythmia estimation system 200 estimates the class corresponding to the beat segment included in the first section of the ECG signal using a first artificial neural network and uses a second artificial neural network.
  • the class corresponding to the first section of the ECG signal is estimated, and the class estimated to correspond to the bit segment included in the first section of the ECG signal and the class estimated to correspond to the first section of the ECG signal are mutually It can perform the verification function.
  • the arrhythmia estimation system 200 may be a digital device equipped with a memory means and equipped with a microprocessor and has computing capabilities. For example, it may be a server system operated on the communication network 100.
  • arrhythmia estimation system 200 The configuration and function of the arrhythmia estimation system 200 according to an embodiment of the present invention will be discussed in detail through the detailed description below.
  • the device 300 is a digital device that includes a function that can communicate after connecting to the arrhythmia estimation system 200, and can be used as a smart patch, smart watch, smart band, or smart glass.
  • a digital device equipped with a memory means, a microprocessor, and computing power, and a sensing means (e.g., contact electrode, etc.) for measuring a certain biological signal (e.g., electrocardiogram signal) from the human body, and It may be a wearable monitoring device that includes display means that provides various information regarding measurement of biological signals to the user.
  • the device 300 may further include an application program for performing functions according to the present invention.
  • These applications may exist in the form of program modules within the device 300.
  • the nature of this program module is the first estimation unit 210, the second estimation unit 220, the verification unit 230, the communication unit 240, the control unit 250, and the overall arrhythmia estimation system 200, which will be described later. It can be similar.
  • at least part of the application may be replaced with a hardware device or firmware device that can perform substantially the same or equivalent functions as necessary.
  • FIG. 2 is a diagram illustrating in detail the internal configuration of the arrhythmia estimation system 200 according to an embodiment of the present invention.
  • the arrhythmia estimation system 200 includes a first estimation unit 210, a second estimation unit 220, a verification unit 230, a communication unit 240, and It may include a control unit 250.
  • the first estimation unit 210, the second estimation unit 220, the verification unit 230, the communication unit 240, and the control unit 250 of the arrhythmia estimation system 200 are among them. At least some of them may be program modules that communicate with an external system (not shown). These program modules may be included in the arrhythmia estimation system 200 in the form of an operating system, application program module, or other program module, and may be physically stored in various known memory devices.
  • program modules may be stored in a remote memory device capable of communicating with the arrhythmia estimation system 200.
  • program modules include, but are not limited to, routines, subroutines, programs, objects, components, data structures, etc. that perform specific tasks or execute specific abstract data types according to the present invention.
  • arrhythmia estimation system 200 has been described as above, this description is illustrative and at least some of the components or functions of the arrhythmia estimation system 200 may be used as a device 300 or a server (not shown) as necessary. ) or may be included in an external system (not shown).
  • the first estimation unit 210 may perform a function of estimating the class corresponding to the bit segment included in the first section of the ECG signal using a first artificial neural network.
  • the first artificial neural network is a bit segment included in a predetermined section of the ECG signal (here, the bit segment may mean a QRS waveform (QRS complex) appearing in the ECG signal; in the ECG signal Detecting the beat segment may be performed by a first artificial neural network, or may be performed by means or methods other than the first artificial neural network) to estimate which class among the classes representing the first type of arrhythmia corresponds. It may be a trained artificial neural network.
  • the first type of arrhythmia may include arrhythmias that can be estimated on a beat segment basis.
  • the first type of arrhythmia includes atrial premature contraction (APC), It may include ventricular premature contraction (VPC), left bundle branch block (LBBB), and right bundle branch block (RBBB).
  • the first estimation unit 210 uses a first artificial neural network to determine that at least one bit segment included in the first section of the ECG signal indicates the first type of arrhythmia. It is possible to estimate which of the classes it corresponds to, and further, if the beat segment does not correspond to any of the classes representing the first type of arrhythmia, it is assumed that the class corresponding to the beat segment is the class representing a normal electrocardiogram. can do.
  • the first estimation unit 210 uses the first artificial neural network to determine four of the five bit segments included in the first section of the ECG signal. It can be assumed that the th beat segment corresponds to a class representing atrial premature contraction (APC), and the first beat segment, the second beat segment, and the third beat segment among the five beat segments included in the first section of the ECG signal. And it can be assumed that the fifth beat segment corresponds to a class representing a normal electrocardiogram.
  • APC atrial premature contraction
  • the first artificial neural network is composed of an input layer, a hidden layer, and an output layer, and is a convolutional neural network (CNN). ), a recurrent neural network (RNN), etc., but is not necessarily limited thereto.
  • CNN convolutional neural network
  • RNN recurrent neural network
  • the second estimation unit 220 may perform a function of estimating the class corresponding to the first section of the ECG signal using a second artificial neural network.
  • the second artificial neural network may be an artificial neural network learned to estimate which class of the classes representing the second type of arrhythmia corresponds to a predetermined section of the ECG signal.
  • the second type of arrhythmia may include arrhythmia that can be estimated through rhythm changes between consecutive beat segments.
  • the second type of arrhythmia includes atrial fibrillation. AFib), Paroxysmal Supraventricular Tachycardia (SVT), and AV Block.
  • the second estimation unit 220 uses a second artificial neural network to determine which class among the classes representing the second type of arrhythmia the first section of the ECG signal corresponds. It can be estimated, and further, if the first section does not correspond to any of the classes representing the second type of arrhythmia, it can be estimated that the class corresponding to the first section is a class representing a normal electrocardiogram.
  • the second estimation unit 220 uses the second artificial neural network to classify the first section of the ECG signal as representing atrial fibrillation (AFib). It can be assumed to correspond to or to a class representing a normal electrocardiogram.
  • AFib atrial fibrillation
  • the second artificial neural network may be configured in parallel with the first artificial neural network, and the same electrocardiogram signal is input to the first artificial neural network and the second artificial neural network configured in parallel. It can be. That is, for the same ECG signal, the first artificial neural network can estimate which class among the classes representing the first type of arrhythmia the beat segment included in the first section of the corresponding ECG signal corresponds, and the second artificial neural network It is possible to estimate which class among the classes representing the second type of arrhythmia the first section of the corresponding ECG signal corresponds.
  • the second artificial neural network is composed of an input layer, a hidden layer, and an output layer, and may be implemented as a convolutional neural network (CNN), a recurrent neural network (RNN), etc.
  • CNN convolutional neural network
  • RNN recurrent neural network
  • the verification unit 230 determines the class estimated to correspond to the bit segment included in the first section of the ECG signal and the class estimated to correspond to the first section of the ECG signal. It can perform a mutual verification function.
  • a case may occur where the class estimated to correspond to the bit segment included in the first section of the ECG signal and the class estimated to correspond to the first section of the ECG signal are not compatible with each other. there is.
  • the class corresponding to the first section of the ECG signal is the class representing atrial fibrillation (AFib).
  • a case may occur where the class corresponding to the beat segment included in the first section of the ECG signal is estimated to be a class representing atrial premature contraction (APC).
  • APC atrial premature contraction
  • class estimation for the first section of the ECG signal or the bit segment included in the first section of the ECG signal may be incorrectly made, and the present invention can solve this error through a mutual verification process.
  • the verification unit 230 determines the class estimated to correspond to the bit segment included in the first section of the ECG signal and the class estimated to correspond to the first section of the ECG signal.
  • Mutual verification is possible, and according to the verification results, if the class estimation for any one of the first section of the ECG signal and the bit segment included in the first section of the ECG signal is determined to be incorrect (i.e., the ECG signal If the class estimated to correspond to the bit segment included in the first section of and the class estimated to correspond to the first section of the ECG signal are not compatible with each other), the bit segment included in the first section of the ECG signal The other can be corrected based on one of the class estimated to correspond to and the class estimated to correspond to the first section of the ECG signal.
  • the class corresponding to the first section of the ECG signal is estimated to be a class representing atrial fibrillation (AFib) by the second artificial neural network (S100), and the class corresponding to the first section of the ECG signal is estimated to be a class representing atrial fibrillation (AFib) by the second artificial neural network.
  • the class corresponding to 11 beat segments is estimated to be a class representing atrial premature contraction (APC) (indicated by "S")
  • the class corresponding to 8 beat segments is estimated to be a class representing atrial premature contraction (APC).
  • each class is estimated (S200) to be a class representing a normal ECG signal (indicated by "N")
  • the verification unit 230 determines the class estimated to correspond to the first section of the ECG signal (i.e., atrial fibrillation). Based on the class representing (AFib), the class estimated to correspond to 11 of the 19 beat segments included in the first section of the ECG signal, that is, the class representing atrial premature contraction (APC), is classified as a normal ECG. It can be corrected (S ⁇ N) by the class it represents (S300).
  • the communication unit 240 performs a function to enable data transmission and reception from/to the first estimation unit 210, the second estimation unit 220, and the verification unit 230. can do.
  • control unit 250 has a function of controlling the flow of data between the first estimation unit 210, the second estimation unit 220, the verification unit 230, and the communication unit 240. can be performed. That is, the control unit 250 according to the present invention controls the data flow from/to the outside of the arrhythmia estimation system 200 or the data flow between each component of the arrhythmia estimation system 200, thereby controlling the first estimation unit 210. , the second estimation unit 220, the verification unit 230, and the communication unit 240 can each be controlled to perform their own functions.
  • the embodiments according to the present invention described above can be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium.
  • the computer-readable recording medium may include program instructions, data files, data structures, etc., singly or in combination.
  • Program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention, or may be known and usable by those skilled in the computer software field.
  • Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. medium), and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc.
  • Examples of program instructions include not only machine language code such as that created by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.
  • a hardware device can be converted into one or more software modules to perform processing according to the invention and vice versa.

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Abstract

Selon un aspect de la présente invention, la présente invention concerne un procédé d'estimation de l'arythmie au moyen d'un réseau neuronal artificiel composite, le procédé comprenant les étapes de : estimation d'une classe correspondant à un segment binaire inclus dans une première section d'un signal d'électrocardiogramme au moyen d'un premier réseau neuronal artificiel ; estimation d'une classe correspondant à la première section du signal d'électrocardiogramme au moyen d'un deuxième réseau neuronal artificiel ; et vérification de la classe estimée pour correspondre au segment binaire inclus dans la première section du signal d'électrocardiogramme et de la classe estimée pour correspondre à la première section du signal d'électrocardiogramme l'une par rapport à l'autre.
PCT/KR2023/012021 2022-08-30 2023-08-14 Procédé, système et support d'enregistrement lisible par ordinateur non transitoire pour estimer l'arythmie au moyen d'un réseau neuronal artificiel composite Ceased WO2024049052A1 (fr)

Priority Applications (2)

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JP2024508976A JP7763441B2 (ja) 2022-08-30 2023-08-14 複合人工ニューラルネットワークを利用して不整脈を推定するための方法、システムおよび非一過性のコンピュータ読み取り可能な記録媒体
US18/606,993 US20240215925A1 (en) 2022-08-30 2024-03-15 Method, system, and non-transitory computer-readable recording medium for estimating arrhythmia using composite artificial neural network

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KR1020220109500A KR102549010B1 (ko) 2022-08-30 2022-08-30 복합 인공 신경망을 이용하여 부정맥을 추정하기 위한 방법, 시스템 및 비일시성의 컴퓨터 판독 가능한 기록 매체
KR10-2022-0109500 2022-08-30

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KR102549010B1 (ko) * 2022-08-30 2023-06-28 주식회사 휴이노 복합 인공 신경망을 이용하여 부정맥을 추정하기 위한 방법, 시스템 및 비일시성의 컴퓨터 판독 가능한 기록 매체
KR20250009287A (ko) 2023-07-10 2025-01-17 주식회사 세미콘네트웍스 소실점 검출 장치 및 소실점 검출 방법과, 이를 이용한 자율 주행 보조 시스템

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